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Meeting 2024 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Computational Model Development, Verification, Validation, and Uncertainty Quantification
Presentation Title Accelerating Defect Predictions in Semiconductors Using Crystal Graphs
Author(s) Arun Kumar Mannodi Kanakkithodi
On-Site Speaker (Planned) Arun Kumar Mannodi Kanakkithodi
Abstract Scope Quick defect predictions are complicated by difficulties in assigning measured levels to specific defects and the expense of large-supercell computations involving charge corrections and advanced functionals. We address this issue by combining density functional theory (DFT) data with crystal Graph-based Neural Network (GNN) models to develop predictive models for defect formation energies (DFE) of native defects and impurities in a variety of zincblende semiconductors, as a function of charge and chemical potential. Using 1500 unique defects and thousands of defect polymorphs, we generate one of the largest computational datasets of > 15,000 defective structures + DFEs and rigorously train multiple GNN models. Best DFE predictions are obtained using Atomistic Line Graph Neural Network (ALIGNN), with RMSE ~ 0.3 eV, accuracy of 98% given the total range of values. ALIGNN models are applied for screening across thousands of hypothetical single defects/dopants and complexes, leading to a library of optoelectronically-active semiconductor defects.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Modeling and Simulation, Computational Materials Science & Engineering

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